"Neural networks" are so-called because they mimic in some key respects the activity and interconnectivity of neurons in the human brain. Artificial neural networks of the type to which the present invention belongs have the characteristic that they are able to "learn" through iterative processing during which the strengths of connections between artificial neurons are altered to reinforce associations between input and output patterns. In one class of such neural networks, regularization has been accomplished by "training" the network to respond with a specific output pattern when a particular input pattern (training pattern) is introduced. Because of its capability to associate a new input pattern with the training pattern "closest" to it by triggering the output pattern associated with the training pattern, neural networks of this class are able to regularize training patterns that have been distorted.
The nature of the distortion is often closely related, in the case of images, to the physical processes involved in the formation of the distorted image to be regularized and, more generally with respect to data sets, to the processes of collection, storage, or transmission. Typical examples of sources of distortion include limitations in the resolution of sensors, motion blur caused by relative movement between sensors and objects, refraction, poor focus, interference from external sources, turbid environments, loss of information during conversion between analog and digital representations, and loss of data during collection, storage, or transmission. Such distortion may make it difficult or impossible, without further processing to identify representative or even critical elements of the image or data.
Prior art neural networks, and other means such as serial statistical analysis, have been used to regularize images or other sets of data that have been distorted. The problem is difficult, however, since the data sets are often very large and optimization across multiple dimensions imposes severe computational burdens. Such extensive computation may not be practicable in many applications in which regularization must be achieved quickly. Moreover prior art systems and methods often are not capable of taking into account local correlations between signal and noise that could reduce the computational burden and increase the speed of regularization.
Even more fundamentally, such prior art often is constrained to operate in homogeneous noise environments; that is, where the data has been uniformly distorted in space and time or where special limitations on the nature of the noise, such as requirements of space-invariance or stationarity, have been imposed or assumed. If such prior art is applied to conditions of inhomogeneous noise, computational times may increase by thousands of times relative to the computational times achievable in homogeneous environments, or the regularization may not be achievable at all. Thus, such prior art is often not a practicable approach where the underlying image model is non-stationary, thereby introducing instabilities into the processed images, or where there are spatial inhomogeneities such as space-variant image functions or non-stationary noise statistics.
Attempts to achieve regularization in inhomogeneous environments have involved both serial statistical analysis and, alternatively, parallel distributed processing such as is embodied in typical neural network designs. Although parallel distributed processing employed in prior art neural network systems and methods has sometimes reduced the computational times involved, the designs of such systems or methods have characteristics that inherently limit the reduction of computational speed while maintaining accuracy. In particular, they contain modules that have parallel computational features but that are linked serially and subject to signal modulation or amplification that is specific for the particular problem being addressed.
Common applications of prior art neural networks and other methods for regularizing images or other sets of data include acoustic imaging, radar analysis, processing of images or other sets of data displayed in visual form, and restoring data that has been corrupted. As above noted, however, such prior art commonly requires either substantial knowledge of the characteristics of the source of the corruption of the image or data, or a source of such corruption that is essentially homogeneous, or both. Moreover, to the extent that such prior art is able to function in an environment of inhomogeneous distortion, identification or restoration of the image may require extensive computation that is not compatible with real-time or near-real-time operation.
Accordingly, it is a general object of the present invention to provide a system and method that overcome the drawbacks of prior art systems and methods, in particular by operating accurately and speedily in environments of inhomogeneous distortion, even if unknown.